Multi-Objective Optimisation of URLLC-Based Metaverse Services

TL;DR

This paper presents a multi-objective optimization framework for URLLC-based Metaverse services, jointly optimizing power, RIS phases, and error probability to minimize cost and latency, using a twin-stage controller with advanced algorithms.

Contribution

It introduces a novel twin-stage control scheme with a meta-learning-based MO-SAC algorithm for joint optimization in Metaverse URLLC networks, considering new KPIs.

Findings

Approaches Pareto optimality between cost and latency.

Uses SGD for accurate user localization.

Employs meta-learning for adaptive optimization.

Abstract

Metaverse aims for building a fully immersive virtual shared space, where the users are able to engage in various activities. To successfully deploy the service for each user, the Metaverse service provider and network service provider generally localise the user first and then support the communication between the base station (BS) and the user. A reconfigurable intelligent surface (RIS) is capable of creating a reflected link between the BS and the user to enhance line-of-sight. Furthermore, the new key performance indicators (KPIs) in Metaverse, such as its energy-consumption-dependent total service cost and transmission latency, are often overlooked in ultra-reliable low latency communication (URLLC) designs, which have to be carefully considered in next-generation URLLC (xURLLC) regimes. In this paper, our design objective is to jointly optimise the transmit power, the RIS phase shifts, and the decoding error probability to simultaneously minimise the total service cost and transmission latency and approach the Pareto Front (PF). We conceive a twin-stage central controller, which aims for localising the users first and then supports the communication between the BS and users. In the first stage, we localise the Metaverse users, where the stochastic gradient descent (SGD) algorithm is invoked for accurate user localisation. In the second stage, a meta-learning-based position-dependent multi-objective soft actor and critic (MO-SAC) algorithm is proposed to approach the PF between the total service cost and transmission latency and to further optimise the latency-dependent reliability. Our numerical results demonstrate that ...